Dataset Information

Deconvolution of in vivo protein-RNA contacts using fractionated eCLIP-seq.

ABSTRACT: Thousands of RNA-binding proteins orchestrate RNA processing and altered protein-RNA interactions frequently lead to disease. Here, we present experimental and computational analysis pipelines of fractionated eCLIP-seq (freCLIP-seq), a modification of enhanced UV-crosslinking and RNA immunoprecipitation followed by sequencing. FreCLIP-seq allows transcriptome-wide analysis of protein-RNA interactions at single-nucleotide level and provides an additional level of resolution by isolating binding signals of individual RNA-binding proteins within a multicomponent complex. Binding occupancy can be inferred from read counts and crosslinking events. For complete details on the use and execution of this protocol, please refer to Biancon et al. (2022).

SUBMITTER: Biancon G

PROVIDER: S-EPMC9676202 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Deconvolution of in vivo protein-RNA contacts using fractionated eCLIP-seq.

Biancon Giulia G Busarello Emma E Joshi Poorval P Lesch Bluma J BJ Halene Stephanie S Tebaldi Toma T

STAR protocols 20221116 4

Thousands of RNA-binding proteins orchestrate RNA processing and altered protein-RNA interactions frequently lead to disease. Here, we present experimental and computational analysis pipelines of fractionated eCLIP-seq (freCLIP-seq), a modification of enhanced UV-crosslinking and RNA immunoprecipitation followed by sequencing. FreCLIP-seq allows transcriptome-wide analysis of protein-RNA interactions at single-nucleotide level and provides an additional level of resolution by isolating binding s ...[more]

PMID: 36595959

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Project description:BackgroundCell composition deconvolution (CCD) is a type of bioinformatic task to estimate the cell fractions from bulk gene expression profiles, such as RNA-seq. Many CCD models were developed to perform linear regression analysis using reference gene expression signatures of distinct cell types. Reference gene expression signatures could be generated from cell-specific gene expression profiles, such as scRNA-seq. However, the batch effects and dropout events frequently observed across scRNA-seq datasets have limited the performances of CCD methods.MethodsWe developed a deep neural network (DNN) model, HASCAD, to predict the cell fractions of up to 15 immune cell types. HASCAD was trained using the bulk RNA-seq simulated from three scRNA-seq datasets that have been normalized by using a Harmony-Symphony based strategy. Mean square error and Pearson correlation coefficient were used to compare the performance of HASCAD with those of other widely used CCD methods. Two types of datasets, including a set of simulated bulk RNA-seq, and three human PBMC RNA-seq datasets, were arranged to conduct the benchmarks.ResultsHASCAD is useful for the investigation of the impacts of immune cell heterogeneity on the therapeutic effects of immune checkpoint inhibitors, since the target cell types include the ones known to play a role in anti-tumor immunity, such as three subtypes of CD8 T cells and three subtypes of CD4 T cells. We found that the removal of batch effects in the reference scRNA-seq datasets could benefit the task of CCD. Our benchmarks showed that HASCAD is more suitable for analyzing bulk RNA-seq data, compared with the two widely used CCD methods, CIBERSORTx and quanTIseq. We applied HASCAD to analyze the liver cancer samples of TCGA-LIHC, and found that there were significant associations of the predicted abundance of Treg and effector CD8 T cell with patients' overall survival.ConclusionHASCAD could predict the cell composition of the PBMC bulk RNA-seq and classify the cell type from pure bulk RNA-seq. The model of HASCAD is available at https://github.com/holiday01/HASCAD .

Dataset Information

Deconvolution of in vivo protein-RNA contacts using fractionated eCLIP-seq.

Publications

Deconvolution of in vivo protein-RNA contacts using fractionated eCLIP-seq.

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